Lindley's paradox is a counterintuitive situation in statistics in which the Bayesian and frequentist approaches to a hypothesis testing problem give different results for certain choices of the prior distribution. The problem of the disagreement between the two approaches was discussed in Harold Jeffreys' 1939 textbook; it became known as Lindley's paradox after Dennis Lindley called the disagreement a paradox in a 1957 paper. Although referred to as a paradox, the differing results from the Bayesian and frequentist approaches can be explained as using them to answer fundamentally different questions, rather than actual disagreement between the two methods. Nevertheless, for a large class of priors the differences between the frequentist and Bayesian approach are caused by keeping the significance level fixed: as even Lindley recognized, "the theory does not justify the practice of keeping the significance level fixed'' and even "some computations by Prof. Pearson in the discussion to that paper emphasized how the significance level would have to change with the sample size, if the losses and prior probabilities were kept fixed.'' In fact, if the critical value increases with the sample size suitably fast, then the disagreement between the frequentist and Bayesian approaches becomes negligible as the sample size increases. Description of the paradox The result of some experiment has two possible explanations, hypotheses and , and some prior distribution representing uncertainty as to which hypothesis is more accurate before taking into account . Lindley's paradox occurs when The result is "significant" by a frequentist test of , indicating sufficient evidence to reject , say, at the 5% level, and The posterior probability of given is high, indicating strong evidence that is in better agreement with than . These results can occur at the same time when is very specific, more diffuse, and the prior distribution does not strongly favor one or the other, as seen below. Numerical example The following numerical example illustrates Lindley's paradox. In a certain city 49,581 boys and 48,870 girls have been born over a certain time period. The observed proportion of male births is thus 49,581/98,451 ≈ 0.5036. We assume the fraction of male births is a binomial variable with parameter . We are interested in testing whether is 0.5 or some other value. That is, our null hypothesis is and the alternative is . Frequentist approach The frequentist approach to testing is to compute a p-value, the probability of observing a